Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

Quanwei Zhou; Zhaokai Zhou; Youwei Guo; Xuejun Yan; Xingjun Jiang; Can Du; Yiquan Ke

doi:10.1016/j.isci.2025.112023

Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

iScience. 2025 Feb 13;28(7):112023. doi: 10.1016/j.isci.2025.112023. eCollection 2025 Jul 18.

Authors

Quanwei Zhou¹, Zhaokai Zhou², Youwei Guo³, Xuejun Yan⁴, Xingjun Jiang³, Can Du³, Yiquan Ke¹

Affiliations

¹ The National Key Clinical Specialty, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
² Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
³ Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
⁴ NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan, China.

Abstract

The growth of glioma is frequently accompanied by a hypoxic microenvironment. Nevertheless, the clinical implications of hypoxia have not been extensively investigated. Single-cell RNA sequencing analysis indicated a heterogeneous communication between different types of cells in the hypoxic microenvironment. Two hypoxia-related glioma subtypes, C1 and C2, show distinct prognostic and molecular differences. Subtype C2 gliomas have more immune and stromal cells, higher immune checkpoint gene expression, and worse prognosis than those in C1. Using machine learning, we developed an 11-gene signature predicting clinical outcomes in six cohorts, validated by RT-qPCR, effectively distinguishing high-risk and low-risk patients and reliably predicting overall and relapse-free survival. Moreover, the risk score is more accurate than conventional clinical variables, molecular characteristics, and 100 previously published signatures. High-risk gliomas show increased CD163, PD1, HIF1A, and PD-L1 expression. We developed a hypoxia-related classification to guide treatment decisions and a reliable prognostic tool.

Keywords: Cancer; Machine learning.